Gastos Casa
28 October, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 2/9/2024 | Comida | 42057 | Tami | Supermercado |
| 4/9/2024 | Agua | 11723 | Andrés | NA |
| 4/9/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 7/9/2024 | Comida | 15960 | Tami | Supermercado |
| 8/9/2024 | Gas/Bencina | 20182 | Tami | Parafina |
| 8/9/2024 | Comida | 55162 | Tami | Supermercado |
| 14/9/2024 | Comida | 16000 | Andrés | NA |
| 14/9/2024 | Comida | 59823 | Tami | Supermercado |
| 20/9/2024 | Electricidad | 22000 | Andrés | NA |
| 23/9/2024 | Comida | 60298 | Tami | Supermercado |
| 26/9/2024 | Rgalo Chepa | 31036 | Tami | NA |
| 29/9/2024 | Gas/Bencina | 15000 | Tami | Bencina Matri Delox |
| 30/9/2024 | Comida | 52577 | Tami | Supermercado |
| 30/9/2024 | Viaje Brasil | 277776 | Tami | Viaje Brasil |
| 30/9/2024 | Viaje Brasil | 277776 | Tami | Viaje Brasil |
| 30/9/2024 | Viaje Brasil | 277776 | Tami | Viaje Brasil |
| 1/10/2024 | Electricidad | 88000 | Andrés | NA |
| 1/10/2024 | Comida | 22895 | Andrés | piwen |
| 2/10/2024 | Viaje Brasil | 277776 | Tami | Cuota que faltaba viaje Brasil |
| 5/10/2024 | Comida | 50577 | Tami | Supermercado |
| 6/10/2024 | Agua | 11723 | Andrés | NA |
| 9/10/2024 | Diosi | 14990 | Andrés | comida diosi nyd 1.5k |
| 12/10/2024 | Comida | 70144 | Tami | Supermercado |
| 19/10/2024 | Comida | 62351 | Tami | Supermercado |
| 24/10/2024 | Electrodomésticos/mantención casa | 13500 | Andrés | lona quitasol |
| 24/10/2024 | VTR | 21990 | Andrés | NA |
| 25/10/2024 | Comida | 8350 | Tami | Supermercado |
| 27/10/2024 | Comida | 78084 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0062e+09 2 4.8951 0.0077 **
## lag_depvar 2.5706e+11 1 2501.1492 <2e-16 ***
## Residuals 7.9242e+10 771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -2084.854 16542.53 0.162861
## 2-0 32056.806 23653.976 40459.64 0.000000
## 2-1 24827.968 19939.331 29716.60 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 250 33729.86 2 26932.43
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## 252 44028.00 2 38081.43
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## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
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## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
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## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
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## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
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## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
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## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
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## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
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## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
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## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
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## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
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## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
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## 460 51554.29 2 48554.29
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## 463 50720.71 2 50490.00
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## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
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## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
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## 523 87244.71 2 79257.86
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## 553 50565.00 2 49242.43
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## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
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## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
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## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
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## 576 59576.00 2 71741.00
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## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
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## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
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## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
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## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
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## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 619 54291.07 22927.627
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2022.69941 4042.00597 -539.65891 2436.69575 -2972.77003 517.60537
## 8 9 10 11 12 13
## -5657.52548 -1185.80401 -3963.92192 -413.66631 -4936.01565 -1603.12971
## 14 15 16 17 18 19
## -893.49620 383.23918 -3238.40435 -372.09155 -2125.07619 6609.67239
## 20 21 22 23 24 25
## -1529.37414 -1207.77476 1476.52861 -1187.19381 234.58384 1694.43798
## 26 27 28 29 30 31
## -7104.03929 950.45674 8194.06059 413.84951 -18.29935 -2404.55719
## 32 33 34 35 36 37
## 1574.14971 4569.40816 1120.87079 2385.19321 -1875.29487 4602.63652
## 38 39 40 41 42 43
## 4300.63683 -2281.08491 -2984.56631 -1110.83480 -10741.30360 7296.84162
## 44 45 46 47 48 49
## 2558.73421 1366.34698 8103.76544 679.34298 6522.33252 6704.28872
## 50 51 52 53 54 55
## -5895.77814 -4803.13052 -5063.14392 -7928.06608 6135.70452 -4075.71468
## 56 57 58 59 60 61
## -4890.76254 3862.25802 890.76198 -30.21392 143.86772 -4995.13434
## 62 63 64 65 66 67
## 18131.32706 3632.10941 -3656.08317 5919.19901 7334.63488 14625.70385
## 68 69 70 71 72 73
## 1671.73733 -13232.50054 -1314.12322 4637.55572 -4908.62116 -4408.30855
## 74 75 76 77 78 79
## -10497.54506 2474.39616 -5394.70398 1072.19636 -6859.51431 557.87299
## 80 81 82 83 84 85
## -2345.26694 -2683.91555 -3921.11303 -525.14391 2325.92530 3770.92754
## 86 87 88 89 90 91
## 481.34704 -481.07141 199.94146 4304.65214 -1163.81131 1150.96102
## 92 93 94 95 96 97
## -2065.23825 -1043.48573 179.03047 275.89876 -7483.28457 2397.95919
## 98 99 100 101 102 103
## -8598.77275 -2930.79301 -4028.48964 -1723.85648 -1248.52343 3193.32565
## 104 105 106 107 108 109
## -2333.51226 2603.30115 -1152.19301 976.78804 2591.91967 -3152.09781
## 110 111 112 113 114 115
## -4718.67285 -842.84496 1910.70769 11698.44642 -1247.04393 2665.57834
## 116 117 118 119 120 121
## 4258.31099 3495.60615 -1108.65375 -4723.04143 -3726.35323 2320.67205
## 122 123 124 125 126 127
## -1733.50459 1341.05543 8857.87862 840.25938 123.91441 -2526.99779
## 128 129 130 131 132 133
## 2652.01086 7047.94664 1003.26411 -8508.11362 1747.95940 4133.16477
## 134 135 136 137 138 139
## -3169.04855 -1421.79115 -854.67583 -3879.93308 1186.06048 -493.67490
## 140 141 142 143 144 145
## -2911.61504 1722.12118 -1878.87126 -7825.93712 2048.28365 -3473.51174
## 146 147 148 149 150 151
## 2110.24327 -252.07965 1027.89506 -355.85678 1355.43348 1188.41094
## 152 153 154 155 156 157
## 3357.20626 -4863.78472 -1172.58341 -3233.28466 5961.34833 9746.20721
## 158 159 160 161 162 163
## -3713.11892 -5057.90798 3326.96621 -86.51807 2413.61853 -6196.40684
## 164 165 166 167 168 169
## -7029.24342 3879.26677 17109.50903 3321.32088 -708.71770 -2754.74926
## 170 171 172 173 174 175
## -1409.31367 3286.95068 -535.79943 -8382.23092 2566.43977 4024.05554
## 176 177 178 179 180 181
## 318.66283 8443.00291 -9566.10905 -3777.03929 -11045.91008 -11529.41458
## 182 183 184 185 186 187
## 956.72533 9010.24755 -1727.26004 5631.62034 6248.61659 12840.77362
## 188 189 190 191 192 193
## 8093.13407 -4413.50062 2120.30359 10020.22470 -2007.29242 -2804.03836
## 194 195 196 197 198 199
## -10634.70276 -6700.35295 906.62413 -5562.12402 -10116.46566 5078.07006
## 200 201 202 203 204 205
## -3383.01189 -2022.85395 -1112.84409 6185.56313 9559.65363 236.48457
## 206 207 208 209 210 211
## 2581.72226 2750.41469 5432.62512 12473.77528 -6066.34627 -11660.58700
## 212 213 214 215 216 217
## -6007.85218 -10917.34474 -5386.18109 1223.78683 -13317.50144 16103.48059
## 218 219 220 221 222 223
## 7491.26338 1194.71462 26351.94726 12140.76840 6930.52370 13615.05604
## 224 225 226 227 228 229
## -4343.49615 -2154.42734 3375.28341 -41.03918 2352.87729 8614.89161
## 230 231 232 233 234 235
## 5434.29704 -2301.35704 -2210.45679 9053.80841 -11890.72449 -7639.60536
## 236 237 238 239 240 241
## -8881.49495 -10425.57164 2770.80193 1038.49788 -8613.78559 -9292.30354
## 242 243 244 245 246 247
## 8805.76074 -8073.90462 2190.91631 -10604.89477 -4341.56004 1138.30876
## 248 249 250 251 252 253
## 711.51389 -12613.24515 3364.95752 1771.83904 3912.74448 1823.88582
## 254 255 256 257 258 259
## -1478.32048 10821.38231 20538.36932 2809.92196 -4662.35193 3730.73654
## 260 261 262 263 264 265
## -2078.89268 3359.96333 -5233.50571 -11261.79402 -5072.01761 -855.22557
## 266 267 268 269 270 271
## -5521.58194 8454.32244 -4625.92620 3852.01033 -2455.35271 4086.17670
## 272 273 274 275 276 277
## 353.12989 6945.06816 -1787.22371 11654.03927 -4984.15448 1338.26832
## 278 279 280 281 282 283
## -761.85558 7464.91863 -5461.22900 -3118.13848 -11637.87353 -3013.79331
## 284 285 286 287 288 289
## 18317.39833 7396.33229 2328.77143 -1037.28542 503.29449 5996.94918
## 290 291 292 293 294 295
## 6467.65388 -19200.67032 -11506.39206 -8453.08212 9357.72440 2735.79339
## 296 297 298 299 300 301
## -1523.94670 27061.23955 9643.69094 4455.91554 9067.50961 2387.74373
## 302 303 304 305 306 307
## -1497.80962 7446.07572 -24758.63770 -3909.32328 -532.82149 -7320.80795
## 308 309 310 311 312 313
## -4298.43668 2620.10738 -9512.99996 -3519.69524 -8465.92522 1310.93284
## 314 315 316 317 318 319
## -3415.00969 1790.56842 -4352.49279 27183.77208 -1102.84992 2916.98398
## 320 321 322 323 324 325
## 10447.28496 5173.01386 31952.70843 4585.19731 -21458.63875 1362.26271
## 326 327 328 329 330 331
## 685.33962 -6883.27477 -2120.20809 -33640.33398 666.46489 -2522.11689
## 332 333 334 335 336 337
## -306.42610 -3383.55197 3878.07262 -664.16968 -7181.27609 -3322.76093
## 338 339 340 341 342 343
## -2391.29078 -7876.67193 3676.95144 -1569.40345 -1937.70442 -1193.87480
## 344 345 346 347 348 349
## -26.24489 271.51477 -1836.83199 -9664.78140 -13398.84592 2162.32897
## 350 351 352 353 354 355
## -4492.27382 -3821.27151 -6139.42920 1602.98530 1216.48583 2567.63488
## 356 357 358 359 360 361
## -3974.20767 -718.42668 467.84781 6792.66414 19.26098 -301.09813
## 362 363 364 365 366 367
## 2316.72009 -3030.11767 -1146.52173 -9009.86155 -4855.65184 -6425.65005
## 368 369 370 371 372 373
## -5141.10239 -7430.10429 4859.47124 181.22706 6918.65564 -7876.90555
## 374 375 376 377 378 379
## -2474.49441 -3595.45788 -2665.94383 -12652.47033 1754.03761 -10803.48561
## 380 381 382 383 384 385
## 5561.95142 9162.04441 2898.69100 -2647.41797 1361.19221 6487.79005
## 386 387 388 389 390 391
## 11119.06705 -6148.72317 -5684.79317 -457.21542 8262.25191 1473.32285
## 392 393 394 395 396 397
## 10872.91401 -10277.17214 2423.42772 351.16379 200.70495 -1014.70391
## 398 399 400 401 402 403
## -917.57297 -14835.99692 8249.25050 -1490.61056 -1673.30427 6689.66297
## 404 405 406 407 408 409
## -8256.18094 -1578.59565 -2804.52208 -6077.83087 -3088.63006 -4134.41124
## 410 411 412 413 414 415
## -8956.89118 5968.38662 1436.44393 -7592.29795 -7881.27576 14060.90720
## 416 417 418 419 420 421
## 3573.37502 4222.24933 -8332.74353 -5002.12463 -2836.85874 2594.42319
## 422 423 424 425 426 427
## -14253.79991 -2969.39325 -9270.71138 2876.34768 6813.26418 6365.93091
## 428 429 430 431 432 433
## -4237.12428 -4354.50553 -4939.76622 -1989.35087 -5909.55135 -6803.95584
## 434 435 436 437 438 439
## -6104.24954 -1530.76430 -991.55919 -5127.39180 2441.04414 4673.17698
## 440 441 442 443 444 445
## -5257.48205 -2342.60220 1393.46057 -4036.49003 2647.06338 -6788.41628
## 446 447 448 449 450 451
## -12295.88371 -4648.48751 9516.69908 -2218.14439 4570.12006 -6083.43179
## 452 453 454 455 456 457
## -1314.14507 190.92799 2825.08458 -12489.35633 3199.72878 -6894.70063
## 458 459 460 461 462 463
## 6352.44066 2804.18079 2279.99490 -4087.65455 1866.37425 -246.45510
## 464 465 466 467 468 469
## 1551.77377 -772.46872 3100.52236 -2905.28195 5552.16256 -7220.98422
## 470 471 472 473 474 475
## -3207.95784 -2434.63552 -4883.54275 2796.09255 7579.52834 -6272.62529
## 476 477 478 479 480 481
## 1256.85968 -6414.10181 -3052.50177 1813.55568 -13141.30574 -9913.72561
## 482 483 484 485 486 487
## -1327.66878 -112.06253 -1106.20847 -1492.22630 -9739.22852 10972.06557
## 488 489 490 491 492 493
## 6051.59469 7202.81286 -5689.82774 5137.80387 9038.04467 5758.63954
## 494 495 496 497 498 499
## -13790.98547 -10817.49710 -3645.22798 -1297.87084 -715.73999 -7819.40368
## 500 501 502 503 504 505
## 446.66567 4114.04486 5310.56280 434.66950 -149.26833 -7469.96135
## 506 507 508 509 510 511
## 369.54282 -5254.44517 1645.18053 -1496.23869 -8355.67884 -769.18538
## 512 513 514 515 516 517
## -2845.88817 -754.34899 1161.00653 -9678.70431 -7914.53713 24160.36930
## 518 519 520 521 522 523
## 9584.84196 5596.40484 -5640.40898 2521.73239 16733.38700 11118.62072
## 524 525 526 527 528 529
## -24542.99801 -5336.49708 -3984.80160 4337.74478 -611.51631 -11354.98501
## 530 531 532 533 534 535
## 4187.52738 13683.42141 -5264.35739 4110.76530 5274.28418 -2092.30353
## 536 537 538 539 540 541
## -4830.16180 -7339.38348 -2332.71707 8099.25094 -132.87285 -8400.14433
## 542 543 544 545 546 547
## 1594.09282 -830.56477 137.25661 -11262.24296 -11246.59782 1897.62320
## 548 549 550 551 552 553
## 6842.45009 -1516.04550 640.08201 -7925.21742 8389.80262 688.89398
## 554 555 556 557 558 559
## -12168.33205 8986.68626 8435.74615 -163.20168 4591.16746 -3856.29985
## 560 561 562 563 564 565
## 13844.53661 21175.06178 -6876.53401 -10048.15856 6460.57257 -110.91781
## 566 567 568 569 570 571
## 3125.29415 -7713.14788 -17602.22448 6450.55988 6193.79683 1635.84007
## 572 573 574 575 576 577
## 2827.69962 1490.48354 -2446.87943 14449.58609 -9976.22753 -6523.04330
## 578 579 580 581 582 583
## 8463.22181 2574.60359 -6836.83560 7250.81966 -4087.23657 -3041.55214
## 584 585 586 587 588 589
## 15451.87119 -14814.74254 8180.92658 -210.78854 -6488.70271 -999.11655
## 590 591 592 593 594 595
## 12.52536 -10891.97524 1604.57943 -7346.90779 2893.25151 8674.42174
## 596 597 598 599 600 601
## -7733.07994 5650.50115 2506.77060 6615.96029 -3457.61815 5899.22337
## 602 603 604 605 606 607
## -8571.90300 2021.26975 1027.82789 2892.88869 1239.22106 138.80261
## 608 609 610 611 612 613
## -6066.86263 7846.24385 -1444.20622 -2825.08095 -3689.66619 -8447.70299
## 614 615 616 617 618 619
## 11773.84903 4700.54870 -9568.03721 11414.63065 5791.45560 -5850.83413
## 620 621 622 623 624 625
## 26111.81084 -13171.82352 -7058.61547 2913.66361 -4410.83990 -10822.43098
## 626 627 628 629 630 631
## 11110.49909 -21867.25073 -2561.39599 8531.81770 10952.62369 -1780.40268
## 632 633 634 635 636 637
## 33064.64319 -6931.33714 5412.89108 5083.93594 -2592.22029 -5647.13448
## 638 639 640 641 642 643
## -2210.99371 -12687.19249 -2446.28275 -2081.60577 -2709.35556 -3039.03058
## 644 645 646 647 648 649
## 1642.42268 4248.26646 16763.36029 18287.57541 554.00884 4468.47282
## 650 651 652 653 654 655
## 10284.97428 19797.44652 337.62510 -28447.93314 -1601.50418 -2536.87094
## 656 657 658 659 660 661
## 1639.32739 -3420.55805 -10832.37430 1495.11356 4050.80659 -1196.55756
## 662 663 664 665 666 667
## 12847.29400 1132.50225 1586.36780 -11918.91157 1194.92477 999.73253
## 668 669 670 671 672 673
## -5355.72476 -7580.82331 1920.34208 -3866.68321 2528.63294 -3534.92733
## 674 675 676 677 678 679
## -9483.83559 -8428.54450 -3083.40458 65.95127 2730.72508 580.98509
## 680 681 682 683 684 685
## -3966.26074 -1941.53909 -1451.43765 -8377.13495 4532.25303 -2379.52091
## 686 687 688 689 690 691
## -1533.83346 450.85647 10710.56339 9672.11720 10419.49342 -9890.87638
## 692 693 694 695 696 697
## -3745.76710 -3317.93158 5703.21178 -10568.63214 -8063.49172 -8743.20312
## 698 699 700 701 702 703
## -6387.81316 -4843.25392 2982.03419 -4518.46733 -2010.11217 4108.05590
## 704 705 706 707 708 709
## 30975.63121 9334.00760 23255.12776 1474.99183 8130.70216 22732.11102
## 710 711 712 713 714 715
## 6363.08613 -18390.54704 4670.35819 -5592.48068 -237.76575 346.45547
## 716 717 718 719 720 721
## -17397.40871 -5375.50508 3228.67712 -3123.55902 -13085.59487 4185.01012
## 722 723 724 725 726 727
## -5657.40625 645.42224 -4034.45650 -12544.73005 1280.65972 -1958.71507
## 728 729 730 731 732 733
## -9870.07143 17183.54164 1668.83455 -2831.82862 5608.40821 -8743.82106
## 734 735 736 737 738 739
## -827.82341 8033.60379 -15465.39034 -6009.26503 7313.92171 -4888.71528
## 740 741 742 743 744 745
## 60.24758 1725.30761 -2061.53349 -5272.74049 6312.03921 -6383.64504
## 746 747 748 749 750 751
## 22595.80709 7700.85932 -2078.72049 -7415.27899 23298.63660 -4429.38527
## 752 753 754 755 756 757
## 1266.65295 -14549.97542 55997.23854 26764.98750 14929.01630 -10811.58323
## 758 759 760 761 762 763
## 10461.84329 7169.54103 5667.04609 -46528.23732 -16267.06936 883.28086
## 764 765 766 767 768 769
## -2602.41387 -3541.64051 122761.79895 19153.33610 43562.17603 22413.46759
## 770 771 772 773 774 775
## 11903.89472 15680.36005 25562.79239 -98977.23262 -6858.08985 -35926.74287
## 776
## 1663.75125
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17246.59 20096.99 24355.80 24073.45 26429.48 23759.11 24476.24 19702.95
## 10 11 12 13 14 15 16 17
## 19439.21 16778.95 17557.30 14282.99 14334.21 14999.62 16698.12 15016.23
## 18 19 20 21 22 23 24 25
## 16052.08 15424.90 22515.37 21598.35 21077.61 22969.77 22294.99 22948.28
## 26 27 28 29 30 31 32 33
## 24796.33 18717.83 20445.94 28292.15 28349.87 28022.41 25649.14 27053.16
## 34 35 36 37 38 39 40 41
## 30900.56 31249.38 32660.15 30167.93 34142.36 37354.08 34406.85 31214.12
## 42 43 44 45 46 47 48 49
## 30060.59 20629.44 28156.69 30595.94 31686.38 38532.23 38026.24 42693.71
## 50 51 52 53 54 55 56 57
## 46934.78 39624.42 34186.72 29203.78 22340.44 28637.57 25214.33 21507.74
## 58 59 60 61 62 63 64 65
## 25921.10 27182.07 27479.42 27891.71 23757.96 40368.03 42214.08 37454.66
## 66 67 68 69 70 71 72 73
## 41666.37 46587.58 57267.83 55279.36 40505.84 38008.87 41030.19 35323.88
## 74 75 76 77 78 79 80 81
## 30770.97 21463.89 24668.99 20590.09 22678.51 17568.27 19585.98 18811.63
## 82 83 84 85 86 87 88 89
## 17838.26 15905.00 17184.22 20796.36 25219.08 26210.07 26235.06 26852.49
## 90 91 92 93 94 95 96 97
## 30982.24 29811.47 30811.95 28874.20 28073.11 28441.67 28848.71 22418.90
## 98 99 100 101 102 103 104 105
## 25437.34 18459.94 17314.78 15353.29 15653.38 16331.53 20809.23 19891.70
## 106 107 108 109 110 111 112 113
## 23406.76 23196.50 24874.51 27754.53 25249.82 21689.27 21965.01 24614.27
## 114 115 116 117 118 119 120 121
## 35491.04 33681.85 35521.40 38523.11 40481.23 38167.04 32982.21 29319.47
## 122 123 124 125 126 127 128 129
## 31404.65 29682.66 30865.55 38473.88 38115.94 37176.43 34036.42 35819.62
## 130 131 132 133 134 135 136 137
## 41223.59 40663.26 31855.04 33121.26 36314.62 32721.22 31106.68 30190.65
## 138 139 140 141 142 143 144 145
## 26743.80 28159.82 27929.19 25612.88 27639.59 26262.79 19857.72 22891.65
## 146 147 148 149 150 151 152 153
## 20715.90 23696.37 24236.96 25829.14 26011.42 27667.45 28969.65 32005.21
## 154 155 156 157 158 159 160 161
## 27470.30 26732.43 24284.94 30185.65 41733.55 40061.91 37423.89 42449.80
## 162 163 164 165 166 167 168 169
## 43859.95 47279.69 42740.53 38042.45 43473.78 59794.25 62008.86 60421.18
## 170 171 172 173 174 175 176 177
## 57243.31 55640.76 58346.37 57369.37 49652.85 52479.52 56226.34 56262.57
## 178 179 180 181 182 183 184 185
## 63399.39 53891.04 50638.34 41436.70 32966.56 36478.75 46593.55 46048.95
## 186 187 188 189 190 191 192 193
## 52008.38 57759.80 68554.87 73843.64 67531.27 67724.92 74803.15 70474.75
## 194 195 196 197 198 199 200 201
## 65992.56 55224.35 49247.80 50673.70 46263.47 38423.50 44855.44 43080.85
## 202 203 204 205 206 207 208 209
## 42718.42 43197.29 49998.92 58898.09 58527.28 60254.01 61911.66 65707.08
## 210 211 212 213 214 215 216 217
## 75184.20 67258.16 55434.00 50036.77 41023.04 37977.36 41094.50 31103.52
## 218 219 220 221 222 223 224 225
## 48096.02 55425.00 56327.91 79118.80 86622.19 88627.66 96227.50 87168.28
## 226 227 228 229 230 231 232 233
## 81160.00 80741.47 77387.69 76548.25 81290.56 82656.36 77085.60 72293.19
## 234 235 236 237 238 239 240 241
## 77953.15 64586.03 56613.64 48555.29 40157.48 44354.07 46509.21 39952.59
## 242 243 244 245 246 247 248 249
## 33625.10 43919.05 38159.51 42099.61 34354.85 33059.26 36718.63 39545.67
## 250 251 252 253 254 255 256 257
## 30364.90 36309.59 40115.26 45315.83 48037.18 47529.19 57841.63 75358.36
## 258 259 260 261 262 263 264 265
## 75173.21 68476.41 69959.89 66176.47 67624.22 61374.94 50637.59 46660.51
## 266 267 268 269 270 271 272 273
## 46870.15 42972.53 51786.50 48055.42 52206.78 50321.25 54393.16 54689.50
## 274 275 276 277 278 279 280 281
## 60713.65 58345.25 68029.01 61947.02 62157.28 60504.51 66253.80 59977.28
## 282 283 284 285 286 287 288 289
## 56537.30 46077.94 44472.89 61724.38 67260.66 67670.57 65085.28 64171.62
## 290 291 292 293 294 295 296 297
## 68177.06 72091.67 53066.96 43157.94 37162.28 47495.21 50740.66 49853.62
## 298 299 300 301 302 303 304 305
## 74077.02 80029.08 80697.49 85315.11 83511.67 78536.35 82007.07 56877.75
## 306 307 308 309 310 311 312 313
## 53134.68 52814.09 46597.29 43803.61 47411.00 39954.84 38675.50 33230.92
## 314 315 316 317 318 319 320 321
## 37019.72 36200.15 40035.92 38018.09 63833.42 61672.16 63297.57 71304.70
## 322 323 324 325 326 327 328 329
## 73694.72 99205.09 97580.92 73383.88 72180.37 70535.85 62478.49 59597.48
## 330 331 332 333 334 335 336 337
## 29511.96 33203.69 33643.71 35966.27 35306.36 41079.88 42156.70 37398.90
## 338 339 340 341 342 343 344 345
## 36612.43 36739.24 32052.91 38058.69 38722.85 38981.59 39858.39 41646.34
## 346 347 348 349 350 351 352 353
## 43470.40 43221.78 36158.42 26715.53 32066.27 30925.99 30515.57 28129.30
## 354 355 356 357 358 359 360 361
## 32813.51 36572.08 41040.78 39227.71 40489.44 42630.34 50034.02 50585.24
## 362 363 364 365 366 367 368 369
## 50787.14 53253.12 50733.66 50177.58 42814.37 40007.94 36180.53 33956.68
## 370 371 372 373 374 375 376 377
## 30009.96 37306.20 39595.77 47490.33 41455.07 40901.60 39437.23 38969.47
## 378 379 380 381 382 383 384 385
## 29826.68 34430.06 27473.76 35702.53 46047.45 49616.99 47888.38 49882.35
## 386 387 388 389 390 391 392 393
## 56109.65 65606.01 58809.51 53271.36 52999.75 60387.82 60911.80 69590.46
## 394 395 396 397 398 399 400 401
## 58683.57 60252.26 59811.87 59295.13 57780.29 56540.43 43283.75 51879.32
## 402 403 404 405 406 407 408 409
## 50878.59 49843.62 56252.32 48786.17 48096.52 46421.26 42093.49 40922.84
## 410 411 412 413 414 415 416 417
## 38984.46 33071.76 40953.70 43883.44 38549.56 33632.09 48521.05 52370.32
## 418 419 420 421 422 423 424 425
## 56304.17 48764.55 45083.57 43758.01 47348.66 35754.25 35483.14 29735.22
## 426 427 428 429 430 431 432 433
## 35331.59 43668.93 50569.12 47330.79 44396.05 41317.64 41205.69 37679.38
## 434 435 436 437 438 439 440 441
## 33813.25 31044.05 32621.99 34473.53 32475.81 37347.68 43560.48 40309.03
## 442 443 444 445 446 447 448 449
## 40014.68 43024.63 40908.22 44902.42 40143.74 31165.49 30001.59 41371.86
## 450 451 452 453 454 455 456 457
## 41053.02 46710.86 42341.86 42691.93 44314.34 48036.93 37899.27 42754.27
## 458 459 460 461 462 463 464 465
## 38172.13 45750.10 49274.29 51897.94 48623.63 50967.17 51168.94 52918.04
## 466 467 468 469 470 471 472 473
## 52415.05 55362.28 52687.41 57744.56 50996.53 48604.64 47189.11 43809.48
## 474 475 476 477 478 479 480 481
## 47570.04 55042.20 49462.57 51167.82 45950.50 44327.59 47163.88 36565.58
## 482 483 484 485 486 487 488 489
## 30119.53 31991.06 34690.92 36182.65 37149.66 30782.93 43327.98 49996.04
## 490 491 492 493 494 495 496 497
## 56834.40 51539.62 56378.38 64021.07 67836.99 54077.07 44643.80 42666.44
## 498 499 500 501 502 503 504 505
## 42990.03 43782.12 38262.33 40664.10 45971.87 51660.19 52370.70 52481.39
## 506 507 508 509 510 511 512 513
## 46175.89 47517.45 43772.25 46530.95 46196.25 39904.61 41037.03 40211.21
## 514 515 516 517 518 519 520 521
## 41318.14 43961.28 36792.97 32066.77 55984.59 64154.88 67812.12 61183.41
## 522 523 524 525 526 527 528 529
## 62524.47 76126.09 83111.00 58031.78 52895.80 49586.26 53970.37 53476.13
## 530 531 532 533 534 535 536 537
## 43648.19 48645.86 61321.21 55835.66 59237.29 63229.73 60278.88 55303.81
## 538 539 540 541 542 543 544 545
## 48758.43 47412.75 55359.16 55109.29 47660.62 49886.85 49713.31 50407.96
## 546 547 548 549 550 551 552 553
## 41046.03 32872.23 37219.12 45345.19 45141.92 46849.79 40852.63 49876.11
## 554 555 556 557 558 559 560 561
## 51032.76 40800.03 50352.11 58224.06 57588.26 61190.16 56952.46 68726.65
## 562 563 564 565 566 567 568 569
## 85434.68 75514.16 64064.43 68488.77 66610.99 67799.01 59359.22 43329.73
## 570 571 572 573 574 575 576 577
## 50346.49 56258.45 57442.59 59520.52 60168.31 57291.41 69552.23 58913.33
## 578 579 580 581 582 583 584 585
## 52629.06 60239.40 61745.12 54831.18 61104.95 56675.98 53717.13 67302.89
## 586 587 588 589 590 591 592 593
## 52714.64 60067.36 59158.70 52873.69 52178.05 52454.40 43159.56 45959.62
## 594 595 596 597 598 599 600 601
## 40579.89 44830.58 53603.94 46927.50 52793.23 55173.75 60849.33 57003.06
## 602 603 604 605 606 607 608 609
## 61822.33 53381.30 55263.46 56040.68 58351.49 58926.20 58466.43 52637.18
## 610 611 612 613 614 615 616 617
## 59706.92 57764.80 54858.67 51560.99 44515.87 56039.31 59931.18 50856.23
## 618 619 620 621 622 623 624 625
## 61270.12 65459.83 58942.19 81195.11 66300.90 58621.48 60626.70 55974.72
## 626 627 628 629 630 631 632 633
## 46299.07 57018.68 37552.82 37412.90 46992.09 57486.69 55529.07 84290.77
## 634 635 636 637 638 639 640 641
## 74465.82 76669.06 78308.22 73028.56 65739.57 62370.05 50261.28 48627.75
## 642 643 644 645 646 647 648 649
## 47518.07 45998.60 44381.43 47061.30 51683.93 66671.71 81112.28 78232.38
## 650 651 652 653 654 655 656 657
## 79137.17 85015.27 98475.09 93227.79 63464.36 60913.30 57864.24 58849.99
## 658 659 660 661 662 663 664 665
## 55286.95 45688.89 48075.91 52398.56 51589.85 63164.64 63042.20 63332.05
## 666 667 668 669 670 671 672 673
## 51774.50 53135.55 54155.15 49488.68 43461.66 46499.97 44096.08 47586.78
## 674 675 676 677 678 679 680 681
## 45336.69 38166.26 32818.26 32815.76 35567.85 40305.16 42568.12 40570.40
## 682 683 684 685 686 687 688 689
## 40594.01 41043.28 35379.32 41715.81 41212.69 41512.29 43510.01 54229.74
## 690 691 692 693 694 695 696 697
## 62696.51 70754.73 60039.62 56042.93 52921.79 58081.63 48363.63 42055.63
## 698 699 700 701 702 703 704 705
## 35944.53 32659.97 31138.25 36651.04 34912.68 35586.09 41525.65 70217.14
## 706 707 708 709 710 711 712 713
## 76382.59 93949.29 90264.44 92862.60 107904.49 106743.83 84080.50 84428.19
## 714 715 716 717 718 719 720 721
## 75756.91 72856.40 70830.69 53541.22 48934.47 52430.42 49932.45 39035.56
## 722 723 724 725 726 727 728 729
## 44609.69 40876.86 43124.46 40997.30 31694.34 35649.43 36275.36 29903.89
## 730 731 732 733 734 735 736 737
## 47991.45 50241.54 48273.31 53933.39 46331.68 46606.54 54596.68 41033.41
## 738 739 740 741 742 743 744 745
## 37441.51 45952.00 42723.04 44227.26 46998.96 46111.17 42526.39 49522.79
## 746 747 748 749 750 751 752 753
## 44538.48 65523.43 70849.43 66954.56 58881.22 78681.53 71748.35 70666.40
## 754 755 756 757 758 759 760 761
## 55887.76 104660.16 121748.98 126342.87 107849.01 110279.89 109526.53 107553.67
## 762 763 764 765 766 767 768 769
## 60180.93 45216.00 47127.27 45750.35 43724.77 152411.95 156853.54 182084.68
## 770 771 772 773 774 775 776
## 185654.96 179586.21 177581.49 184470.95 81579.66 72158.89 38497.96
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8129
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.895051 0.7497032 3.837018
## t2* 2501.149181 163.8176680 849.856566
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.090001 4.819479 13.08954
## 2 lag_depvar 1495.733729 2532.673404 4277.22991Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Oct 28 00:50:02 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Oct 28 00:50:10 2024
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## =-=-=-=-= Iteration 4000 Mon Oct 28 00:50:17 2024
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## =-=-=-=-= Iteration 8000 Mon Oct 28 00:50:32 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Oct 28 00:50:40 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Oct 28 00:50:47 2024
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## =-=-=-=-= Iteration 14000 Mon Oct 28 00:50:55 2024
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## =-=-=-=-= Iteration 16000 Mon Oct 28 00:51:02 2024
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 4.930333 | 5.195333 | 5.410333 | 5.849167 | 6.427707 |
| Comida | 331.004333 | 366.009167 | 312.386750 | 317.896583 | 343.830241 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 88.038778 | 38.104750 | 47.072333 | 29.523000 | 43.172500 |
| Enceres | 30.542000 | 18.259750 | 24.219750 | 14.801167 | 24.661897 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.601086 |
| Gas/Bencina | 59.056889 | 42.636000 | 45.575000 | 13.583667 | 33.795655 |
| Diosi | 33.785444 | 55.804250 | 31.180667 | 52.687833 | 42.079103 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 19.547778 | 12.829167 | 25.156667 | 19.086917 | 19.175138 |
| Netflix | 1.855222 | 8.713833 | 7.151583 | 7.028750 | 6.730931 |
| Otros | 91.000000 | 5.481667 | 5.000000 | 0.000000 | 16.289310 |
| Total | 659.760778 | 563.738000 | 505.988083 | 474.453167 | 544.763569 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2505, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-11-09 00:04:58 sería de: 38.083 pesos// Percentil 95% más alto proyectado: 41.115,45
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37981.42 | 37981.22 |
| Lo.80 | 37982.19 | 37982.06 |
| Point.Forecast | 38083.13 | 38346.00 |
| Hi.80 | 39804.50 | 43105.88 |
| Hi.95 | 40746.71 | 45625.62 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,1) with drift
##
## Coefficients:
## ma1 drift
## -0.9002 5.5382
## s.e. 0.1194 3.3862
##
## sigma^2 = 40652: log likelihood = -450.41
## AIC=906.83 AICc=907.21 BIC=913.44paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 xreg
## 0.2835 32.5902
## s.e. 0.1389 0.9371
##
## sigma^2 = 39102: log likelihood = -455.03
## AIC=916.06 AICc=916.43 BIC=922.72forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 925.2204 | 887.6049 | 820.4729 |
| Lo.80 | 1064.6591 | 1031.6936 | 928.3060 |
| Point.Forecast | 1328.0651 | 1303.8837 | 1172.1812 |
| Hi.80 | 1591.4711 | 1576.0738 | 1521.8749 |
| Hi.95 | 1730.9098 | 1720.1625 | 1747.4344 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))